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README.md

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----
-license: mit
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+---
+language: en
+license: mit
+library_name: pytorch
+tags:
+- continual-learning
+- catastrophic-forgetting
+- information-geometry
+- spectral-methods
+- computer-vision
+metrics:
+- accuracy
+---
+
+# Anastrophic Regularization CNN (Split-MNIST)
+
+This model card hosts the weights for a CNN trained using **Anastrophic Regularization ($\mathcal{R}_{ana}$)**, a novel approach to mitigate catastrophic forgetting in sequential learning tasks.
+
+## Model Description
+Anastrophic Regularization is derived from **Anastrophic Theory**, a mathematical framework for analyzing discrete periodic systems. Unlike standard $L_2$ decay or EWC, this method preserves the structural "Harmonic Memory" of the network by guiding weight evolution along Fisher-Rao geodetic paths.
+
+### Key Advantages
+* **Maximum Plasticity**: Weights adapt to new tasks while maintaining the global periodic functional invariants of previous ones.
+* **100% Data-Free**: Operates strictly in the spectral domain via Fast Fourier Transforms (FFT). No access to previous training data is required.
+* **Privacy Preserving**: Ideal for environments with data-retention constraints where EWC or Replay buffers are not feasible.
+
+## Intended Use
+This specific model serves as a benchmark for **Continual Learning**. It was trained on the Split-MNIST dataset:
+1. **Task A**: Digits 0-4
+2. **Task B**: Digits 5-9 (Trained using $\mathcal{R}_{ana}$ to prevent forgetting Task A).
+
+## Evaluation Results
+The model achieves the following performance:
+* **Task B (Current) Accuracy**: ~86.69%
+* **Task A (Retained) Accuracy**: ~71.16%
+
+## Mathematical Formulation
+The weights were optimized using the following objective:
+
+$$\mathcal{R}_{ana}(W) = \lambda(1 - \Phi(Spec(W))) + \eta BB(W, W_{prev})$$
+
+## Citation and Full Paper
+For the complete theoretical framework, proof of the Fisher-Rao geodetic paths, and the original publication, please refer to:
+
+**Zenodo Repository:** [https://zenodo.org/records/18699347]
+
+**GitHub Implementation:** [https://github.com/MituMath/Anastrophic-Regularization-PyTorch]